Abstract
MATERIALS AND METHODS
A discovery cohort of 1,598 patients (10,485 lesions) and an external validation cohort of 2,440 patients (6,597 lesions) underwent portal-venous-phase CT. After manual segmentation, lesion-level radiomic features were standardized and embedded using t-distributed stochastic neighbor embedding. Bayesian-optimized agglomerative clustering defined morphology-based groups. Concordance with the primary tumor site (lineage) and anatomical environment was quantified using bootstrapped adjusted Rand indices (ARI); the silhouette score assessed clustering quality. Feature-class (shape, intensity, texture) and mask-erosion experiments probed mechanistic drivers.
CONCLUSION
Across organs and tumor types, tumor morphological phenotype on CT imaging is largely driven by a host tissue-related environmental "imprint" rather than the primary tumor site.
RESULTS
Six morphological clusters were identified in the discovery set (silhouette = 0.44). Morphology aligned more strongly with environment (mean ARI = 0.37) but poorly with lineage (mean ARI = 0.04; p < 0.010); this pattern held externally. In solid organ metastases, environment dominance was even stronger (mean ARI = 0.60 versus 0.05; p < 0.010). Intensity and texture drove the morphological association with anatomical environment (ARI = 0.64-0.56) more than shape (ARI = 0.06). When the periphery of the tumor was eroded, the same patterns were observed, implicating the tumor core.
RELEVANCE STATEMENT
Context-aware modeling is essential for reliable radiomic biomarkers and could motivate a two-step AI pipeline that first identifies the organ habitat and refines lineage-specific predictions.
KEY POINTS
In a large, multicenter cohort, tumors exhibited distinct morphological clustering. These clusters did not align with primary tumor sites (ARI = 0.04). Stronger associations emerged between morphological clusters and the local anatomical environment (ARI = 0.37). Stratification by lesion type revealed even stronger associations between local anatomical context and solid organ metastases (ARI = 0.60).
OBJECTIVE
Radiogenomics promises noninvasive tumor profiling; however, the extent to which imaging morphology reflects tumor lineage versus host-organ milieu remains unclear. This study aimed to quantify the relative influence of tumor type and anatomical environment on contrast-enhanced computed tomography (CT) radiomic phenotypes.